1. Field of the Invention
The embodiments described herein relate generally to methods and systems for collecting and processing images in optical coherence tomography.
2. Description of Related Art
Current trends in ophthalmology make extensive use of 3D imaging and image processing techniques. Such images may be utilized for diagnosing diseases such as glaucoma, and other medical conditions affecting the human eye. One of the challenges posed by the current technological advances in imaging techniques is the efficient and meaningful processing of the massive amounts of data collected at ever increasing imaging rates. Some approaches have been to convert a 3D data set into a manageable two-dimensional (2D) image that then can be analyzed. Traditionally, a technique that has been used for data reduction from a 3D data set into a 2D image is that of 2D ‘En Face’ image processing. (See for example, Bajraszewski et al., [Proc. SPIE 5316, 226-232 (2004)], Wojtkowski et al., [Proc. SPIE 5314, 126-131 (2004)], Hitzenberger et al., [Opt Express. October 20; 11 (21):2753-61 (2003)], U.S. Pat. No. 7,301,644, or U.S. Pat. No. 7,505,142). This technique includes the summing of the intensity signals in the 3D data set along one direction, preferentially the Z-direction hereby identified with the axial direction of an Optical Coherence Tomography (OCT) scan, between two retinal tissue layers. The summation takes place among voxels having the same XY position. Typically, voxels located outside of the layers of interest are ignored during processing.
One common problem with this type of ‘En Face’ image processing technique and other volume rendering techniques is the appearance of artifacts created by the involuntary motion of the subject's eye while a data set is being collected. The movement introduces relative displacements of the collected images, so that physical features end up appearing discontinuous in the resulting 3D data set, rendering the entire set unreliable.
Another challenge that commonly occurs in such image processing is that of correlating a sequence of 3D data sets from a given sample, the different data sets having been collected during different imaging sessions spanning a long period of time. The sample can be a subject's eye, a selected vascular structure, or other selected region of interest. In this case, the lack of an efficient and reliable method for correlating each 3D data set to the same physical feature in the sample prevents an accurate assessment of the evolution of the images over time. In ophthalmology, for instance, such a change can be utilized to evaluate certain diseases or conditions in the subject.
What is needed is a better image processing technique that is capable of producing accurate and meaningful information.